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Wooden utility poles are vulnerable to degradation and decay, which requires maintenance or replacement. The strengthening and retrofitting techniques for wooden poles are either prone to corrosion or encountering installation difficulties. However, the use of sprayed fiber-reinforced polymer (FRP) composites seems to be a viable solution as it has proven its efficiency and applicability for reinforced concrete members and connections. This study includes a comprehensive experimental program where the mechanical properties of the sprayed-glass FRP (GFRP) composite was evaluated in terms of tensile, compressive and shear strength, in addition to its bond strength to wood and confinement efficiency. Afterwards, the results of the material testing phase were implemented on full-scale old utility poles to evaluate their structural performance with varying composite thicknesses and sprayed zone lengths. The behavior of the retrofitted poles reflected remarkable effectiveness for the sprayed-GFRP composite and highlighted the need for a design model for the optimum length for the sprayed zone. Two simplified analytical models were introduced which predicted the failure loads and locations for the tested poles and estimated the required length for the retrofitted zone, which all agreed well with the experimental results of the tested poles.

Low velocity impacts may cause damages to the composite material. Piezoelectric transducers can be mounted onto the plate surface to monitor the impact by recording the impact induced guided waves. In this study, an impact localization algorithm based on 1D convolutional neural network is proposed. To test the effectiveness of the proposed algorithm, 4 PZTs were glued to a composite plate to monitor a region of 200 mm × 200 mm, and an impact experiment was conducted by dropping a glass ball at 117 different locations on the plate. The time-domain guided wave signals obtained by 4 PZTs were used as input to the 1D convolutional neural network. The output of the network was the coordinates of impact. The trained network was afterwards applied to locate impacts at unseen locations, and the mean localization error is 15.2 mm.

Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

Runoff prediction serves as the cornerstone for the effective management, allocation, and utilization of water resources, playing a key role in hydrological research. This study employs a newly reported deep learning model, Mamba, to forecast river daily runoff and compared the proposed model with various benchmark methods, including statistical models, machine learning methods, recurrent neural networks, and attention-based models. Application of these models is implemented on three hydrological stations situated along the middle and lower reaches of the Mississippi River. Daily runoff from 1983 to 2023 were used to build the model for 7-day prediction. Findings demonstrate the superiority of the Mamba model over its counterparts, showcasing its potential as a backbone model. In response to the necessity for a more lightweight approach, a refined variant of the Mamba model is proposed, called LightMamba. LightMamba incorporates partial normalization and MPM (Multi-Path-Mamba) to enhance its efficacy in discerning nonlinear trends and capturing long-term dependencies within the streamflow data. Notably, LightMamba achieves commendable performance with an average NSE of 0.904, 0.907, and 0.900 on the three stations. This study introduces an innovative backbone model for time series forecasting, which offers a novel approach to hybrid modeling for future daily runoff prediction. [ABSTRACT FROM AUTHOR]

Recycled fine aggregate (RFA) extracted from construction and demolition (C&D) waste is characterized by high porosity, strong water absorption, and significant water release. It can not only mitigate the excessive autogenous shrinkage of ultra-high-performance concrete (UHPC), but also alleviate the over-exploitation of quartz sand. Therefore, this study proposes a new economic building material for preparing UHPC. It uses RFA with different saturation degrees (0 %, 50 %, and 100 %) to replace quartz sand (QS) and investigates the workability, compressive strength, autogenous shrinkage, resistance to chloride ion penetration, and microstructure of UHPC. The findings indicate that the degree of saturation and content of RFA are the key factors that influence the compressive strength and autogenous shrinkage of UHPC. The early autogenous shrinkage of UHPC is significantly reduced as the saturation degree and content of RFA increase, while the compressive strength is not obviously affected. Overall, using RFA with a 20 % replacement ratio and 100 % saturation degree is beneficial for mitigating autogenous shrinkage and enhancing the 28-day compressive strength. Compared with UHPC without RFA, the 28-day compressive strength increased by 16.6 %, while its 7-day autogenous shrinkage decreased by 90.47 %. Furthermore, the incorporation of 20 % saturated RFA shows the best internal curing effect, which improves the hydration level and reduces the porosity of UHPC. This study successfully provides a practical method for preparing environmentally friendly and low-shrinkage UHPC using RFA with different saturation degrees.

This paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a database containing 579 sets of RAC carbonation test data was developed. Twelve parameters representing material characteristics and environmental conditions were input, along with one output parameter. On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. Different types of analysis, including statistical measures, shapley additive explanations (SHAP) sensitivity analysis, SHAP parametric study, and comparison study, were used to examine the performance of the developed models and the effects of the input parameters on predictions. The results show that the extreme gradient boosting (XGB) model exhibited the highest accuracy with an R² of 0.99 and a mean absolute percentage error (MAPE) of 6.632 on the training set, and an R² of 0.953 and a MAPE of 13.243 on the test set. The variable importance analysis shows that the carbonation depth for RAC was determined by both internal and external factors. The top five factors impacting RAC carbonation depth are exposure time, water-to-binder ratio, CO2 concentration, coarse aggregate density, and cement content. RAC carbonation depth correlates positively with the former three factors. In contrast, it exhibits a negative correlation with the remaining two variables. In addition, a graphical user interface (GUI) for RAC carbonation depth prediction was designed.

Driving evaluations aim to ensure adequate skills; however, feedback beyond pass/fail is needed for improvement. Therefore, the goal of this study was to inform driving feedback report design to ensure ease of use and understandability while motivating improvements. Participants ages 18–25 years (n = 521) were recruited from CloudResearch Prime Panels to rate one of nine report design conditions with various combinations of five key features: performance summary presence, action plan (AP) length, AP order, AP grading system, and peer comparison presence; they then completed questionnaires. Participants were more motivated to improve when a summary was present (p = 0.02); they rated reports easier to use if they had a long AP (p = 0.01), a short AP paired with a summary (p = 0.007), or an AP with a number grade (p = 0.016); and they rated reports easier to understand if they had a short AP (p = 0.002) or an AP ordered by worst-to-best performance (p = 0.05). These results suggest that feedback reports designed with a performance summary and short, targeted action plan starting with the biggest area for improvement are likely to motivate action to improve driving skills while being easy to use and understand. Future research should evaluate the effect of such a redesigned report on driving outcomes among young drivers.

The stress path is a critical factor affecting the mechanical properties of saline soils. In order to study the mechanical properties of saline soils under different stress paths, in situ saline soils in the Qian’an area of western Jilin province were selected for this study, and triaxial shear tests under six different stress paths were conducted, including the consolidated undrained triaxial test under the conventional stress path under the isobaric consolidation condition; the consolidated drained triaxial test under the conventional, equal p, reduced p, and increased p stress paths under the isobaric consolidation condition; and the consolidated drained triaxial test under the conventional stress path under the K0 consolidation condition. The effects of the consolidation conditions, drainage conditions, and stress paths on the mechanical properties of in situ saline soils were investigated. The results reveal that the stress–strain relationship curves of soil samples decrease continuously in the order of increased p, conventional, equal p, and decreased p, and they all show the characteristics of strain hardening. The stress path curves have the same slope under the same stress path. For different confining pressures, only the relative positions of the curves are different. Under the conventional stress path, the slope is 1; under the increased p stress path, the slope is 1/3; under the equal p stress path, the slope is 3; and under the decreased p stress path, the slope of the curve is −1. For the same confining pressure, the magnitude relationships of shear strength under the different stress paths are as follows: increased p > conventional > equal p > decreased p. For the cohesion c and internal friction angle φ, the consolidation condition has a greater effect on the cohesion c and a smaller effect on the internal friction angle φ; the drainage condition has a smaller effect on the cohesion c and a larger effect on the internal friction angle φ; and the stress paths have a greater effect on both cohesion c and internal friction angle φ.

The freeze-thaw cycle is an important external factor affecting the hydromechanical characteristics of saline soil in cold regions. Due to the presence of water and salt, it has a greater impact on stability. The construction of various projects, such as ditch fills and road subgrades, has mostly used disturbed soils. Therefore, this article takes remolded saline soil in Qian’an, Jilin Province, China, as the research object to evaluate the action of freeze-thaw cycles on the critical state line, effective stress path, pore water pressure-strain relationship, stress-strain relationship, shear strength index, and other mechanical properties via a freeze-thaw cycle test and a consolidated undrained triaxial shear test (CU). The experimental results show that regardless of whether the soil specimen undergoes a freeze-thaw cycle, its stress-strain relationship shows characteristics of strain hardening, while, as the number of freeze-thaw cycles increases, the shear strength gradually decreases. As both the confining pressure and number of freeze-thaw cycles increase, the pore water pressure increases, as does the pore water pressure coefficient in shear failure. Under the action of freeze-thaw cycles, on the p′−q plane of the stress space, the effective stress path gradually moves to the lower left side. Both the effective stress path and the pore water pressure characteristics indicate that the degree of consolidation of the soil specimens continuously decreases as the number of freeze-thaw cycles increases. The position of the critical state line gradually lowers, and the critical state stress ratio decreases. The effective stress strength index can more accurately reflect the comprehensive influence of freeze-thaw cycles and confining pressure on the mechanical characteristics of soils than the total stress strength index. Logistic functions can be used to fit and predict the degradation law of the internal friction angle and cohesion.